DreamScape: Browser-Based 3D World Builder Powered by Claude Code and MCP
What DreamScape Does
DreamScape is a browser-based 3D world builder powered entirely by Claude Code through the Model Context Protocol (MCP). Users describe what they want in natural language (e.g., "add a castle on that hilltop," "spawn a dragon and make it fly around") and Claude builds it while they're standing in the scene.
Technical Implementation
The system uses Claude Code to control 30 MCP tools that handle:
- Terrain generation
- Procedural model creation via Blender
- Lighting and weather systems
- Physics and animations
- Scripting and component management
- Asset uploads
Key MCP Integration Features
From a Claude/MCP perspective, several technical details stand out:
- Claude sees the full scene graph and spatial layout before placing anything
- It can attach PlayCanvas scripts to entities for behaviors like patrol paths, physics interactions, and particle effects
- The
dreamscape_evaltool lets Claude execute arbitrary JavaScript in the 3D runtime and read results back dreamscape_validate_placementchecks ground contact via raycast before placing objects- Claude handles entity metadata, component management, and asset uploads through discrete MCP tools
Collaboration and Interaction
The platform includes voice chat integration where users can speak to Claude in the scene and receive TTS responses. Multiple people can join the same session and watch Claude build in real time. The source mentions an active multiplayer session featuring a boss fight with a weaponed dragon.
The tool is currently free with no signup wall. The developers are seeking feedback on how Claude handles spatial reasoning and multi-step scene construction, particularly from those experimenting with MCP tool use.
📖 Read the full source: r/ClaudeAI
👀 See Also
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Governor: A Claude Code Plugin to Cut Token Waste via Output Compression, Context Slimming, and Tool Filtering
Governor is a Claude Code plugin that reduces token/context waste through compact professional output, memory file compression, tool-output filtering, and drift guardrails. Benchmarks show 55.5% output token savings vs control.
Humanizer Pipeline Open-Sourced: Six-Step Markdown File for AI Text Post-Processing
A single Markdown file implements a six-step pipeline to detect and rewrite AI-generated text, with channel awareness, voice calibration, severity gates, and self-audit passing.
Steelman R5: Fine-tuned 14B Model Outperforms Claude Opus on Ada Code Generation
A developer fine-tuned Qwen2.5-Coder-14B-Instruct using QLoRA on a compiler-verified dataset of 3,430 Ada/SPARK instruction pairs, achieving 68.6% compilation rate on a custom benchmark versus Claude Opus 4.6's 42.1%. The model is available via Ollama and fits in 12GB VRAM.